Bars to Handlebars

The Effect of Micromobility on DUIs

Margaret Bock

Goucher College

Alexander Cardazzi

Old Dominion University

Jonathan Hall

University of Alabama

Conor Lennon

Rensselaer Polytechnic Institute

Introduction

In general, transportation in the United States is dominated by automobile. However, recent innovations (e.g. rideshare) have provided alternative options that may reduce vehicle dependency. One of the most recent of these innovations is micromobility.

For example: DC has conventional “docked” bike-share (via Capital Bikeshare, launched in 2010), “dockless” bike-share (via Jump (purchased by Lime), launched in 2017) and e-scooters (via Lime, launched in 2018).

. . .

According to the Bureau of Transportation Statistics, micromobility options have been in about 400 “cities” across the United States.

Literature

The effects of alternative transportation options on impaired driving outcomes is a topic that has received quite a bit of attention (Fell et al. 2020). Rideshare, in particular, accounts for a significant portion of this interest over the past half-decade.

Most research to date tends to agree that rideshare has marginally reduced alcohol-related driving fatalities (Dills and Mulholland 2018; Anderson and Davis 2021; Peck 2017; Burton 2021)
Zhou (2020) used BRFSS to measure the effect of rideshare on binge drinking (increase), drinking days (no change), and drunk driving incidents (no change).

Literature

Little research exists about micromobility, however. Button, Frye, and Reaves (2020) is, to our knowledge, the only micromobility paper published in an economics journal.

. . .

That said, the two closest papers to our topic are:

Yang et al. (2020) used media reports and machine learning to perform a descriptive analysis of 169 E-Scooter-involved crashes.
Jackson and Owens (2011) examined a temporal expansion of train services in Washington DC, and find an increase in alcohol-related arrests but a decrease in DUIs.

Research Question

In this study, we ask how the introduction of micromobility impacts DUI arrests. We hypothesize that, like rideshare, micromobility could provide an alternative to driving (an automobile) under the influence.

Data

Bureau of Transportation Statistics
- City-by-year micromobility information
- Start/end months for docked, dockless, and e-scooter systems

Federal Bureau of Investigation, Crime Data Explorer
- Agency-by-month counts of particular crimes
- Agencies and BTS cities were then matched to one another

Final dataset covers 268 agencies from 2010 to 2019
- Docked Bikeshare: 82 (30.6% of agencies)
- Dockless Bikeshare: 74 (27.6% of agencies)
- E-Scooters: 78 (29.1% of agencies)
- Uber entry dates and ACS information for each agency

Micromobility Rollout

Overlap in Modes

Distribution of the Maximum Number of Firms in a City by Mode
	Unique #'s	Mean	SD	Median	75th %ile	Max
Docked	3	0.4	0.5	0	1	2
Dockless	5	0.5	0.7	0	1	5
E-Scooter	10	1	2	1	2	10

Overlap in Modes

Micromobility Modalities available in Jan. 2020
Num. Micromobility Modes	0	1	2	3
Num. Cities/Agencies	191	134	53	6

Percent of Cities with a Pre-Existing Micromobility Option on Entry of Another
Mode	Docked	Dockless	E-Scooters	Total
Docked		0.7	2.1	141
Dockless	23.2		26.8	138
E-Scooters	21.4	19.2		229

Estimation Sample

In our estimation sample, we only consider agencies:

within “central” counties of metropolitan statistical areas
with an identified Uber entry date

For mode-specific estimations we only consider agencies exposed to that mode first, and drop observations of control agencies once they obtain other types of micromobility. We can also drop observations of agencies following firm exits, but our results do not change much.

Method

We begin by estimating a Poisson regression of the form:

\[\text{DUI}_{at} = \color{red}{\delta} M_{at} + X_{at}\beta + \alpha_a + \tau_t + \epsilon_{at}\]

where:: \(M_{at}\) is an indicator variable equal to one if the agency operates in an area with micromobility options.; \(\alpha_a\) and \(\tau_t\) represent agency and time (year-month) fixed effects.; \(X_{at}\) is a vector of controls including arrests for drunkenness, liquor law violations, Uber entry, and demographic information (income, age, percent white, education).; Outcomes currently include arrests for DUIs and Drug Possession (falsification).; Variations include state-by-year and agency-by-month fixed effects.

Method

We also use the new Local Projections Difference-in-Differences (Dube et al. 2023) estimator to investigate the effects of micromobility.

\[\text{DUI}_{a,t+h} - \text{DUI}_{a,t-1} = \color{red}{\delta_h^{LP}}\Delta M_{a,t} + \tau_{t}^{h} + e_{at}^{h}\]

using only observations where \(\Delta M_{at} = 1\) (newly treated) or \(M_{a,t+h} = 0\) (clean control). This is akin to the “stacking” estimator in Cengiz et al. (2019).

Results - Event Study, Any

Results - Event Study, Docked

Results - LPDiD, Docked

Results

We estimate a \(\approx\) 10% reduction in DUIs per month due to the introduction of micromobility.

While this may seem like an attractive headline, the interpretation is delicate and unlike that of rideshare. A reduction of DUIs indicates less drunk driving, but the actual safety implications are much less clear.

Future Directions

Currently working on exploring crash data at a county-month level across ~40 states.
Hoping to add in health outcome data (HCUP)
What happens when micromobility is removed?

Summary

We supply one of the first papers to examine the externalities of micromobility.
Using TWFE and LPDiD, we find relatively large reductions in DUI arrests.
Ideally, we could observe injuries, crashes, or non-fatal outcomes.
There’s more work to be done on micromobility!

Bibliography

Anderson, Michael L, and Lucas W Davis. 2021. “Uber and Alcohol-Related Traffic Fatalities.” Working Paper 29071. Working Paper Series. National Bureau of Economic Research. https://doi.org/10.3386/w29071.

Burton, Anne M. 2021. “Do Uber and Lyft Reduce Drunk-Driving Fatalities.” https://annemburton.com/pages/working_papers/burton_2nd_year_paper_2021_08_20.pdf.

Button, Kenneth, Hailey Frye, and David Reaves. 2020. “Economic Regulation and e-Scooter Networks in the USA.” Research in Transportation Economics 84: 100973. https://doi.org/https://doi.org/10.1016/j.retrec.2020.100973.

Cengiz, Doruk, Arindrajit Dube, Attila Lindner, and Ben Zipperer. 2019. “The Effect of Minimum Wages on Low-Wage Jobs*.” The Quarterly Journal of Economics 134 (3): 1405–54. https://doi.org/10.1093/qje/qjz014.

Dills, Angela K., and Sean E. Mulholland. 2018. “Ride-Sharing, Fatal Crashes, and Crime.” Southern Economic Journal 84 (4): 965–91. https://doi.org/10.1002/soej.12255.

Dube, Arindrajit, Daniele Girardi, Oscar Jorda, and Alan M Taylor. 2023. “A Local Projections Approach to Difference-in-Differences Event Studies.” Working Paper 31184. Working Paper Series. National Bureau of Economic Research. https://doi.org/10.3386/w31184.

Fell, James C., Jennifer Scolese, Tom Achoki, Courtney Burks, Allison Goldberg, and William DeJong. 2020. “The Effectiveness of Alternative Transportation Programs in Reducing Impaired Driving: A Literature Review and Synthesis.” Journal of Safety Research 75: 128–39. https://doi.org/https://doi.org/10.1016/j.jsr.2020.09.001.

Jackson, C. Kirabo, and Emily Greene Owens. 2011. “One for the Road: Public Transportation, Alcohol Consumption, and Intoxicated Driving.” Journal of Public Economics 95 (1): 106–21. https://doi.org/10.1016/j.jpubeco.2010.09.010.

Peck, Jessica Lynn. 2017. “New York City Drunk Driving After Uber.” https://academicworks.cuny.edu/gc_econ_wp/13/.

Yang, Hong, Qingyu Ma, Zhenyu Wang, Qing Cai, Kun Xie, and Di Yang. 2020. “Safety of Micro-Mobility: Analysis of e-Scooter Crashes by Mining News Reports.” Accident Analysis & Prevention 143: 105608. https://doi.org/10.1016/j.aap.2020.105608.

Zhou, You. 2020. “Ride-Sharing, Alcohol Consumption, and Drunk Driving.” Regional Science and Urban Economics 85: 103594. https://doi.org/10.1016/j.regsciurbeco.2020.103594.